Overview

Dataset statistics

Number of variables100
Number of observations600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory489.6 KiB
Average record size in memory835.6 B

Variable types

Categorical95
Numeric5

Alerts

NCH3 is highly overall correlated with Ammonium and 2 other fieldsHigh correlation
–CH3 is highly overall correlated with Ammonium and 2 other fieldsHigh correlation
–CF2– is highly overall correlated with Trz11 and 3 other fieldsHigh correlation
Im13 is highly overall correlated with aNCH3 and 1 other fieldsHigh correlation
Im123 is highly overall correlated with aCCH3High correlation
Py13 is highly overall correlated with aCCH3High correlation
Py135 is highly overall correlated with aCCH3High correlation
Pyr11 is highly overall correlated with cycNHC3 and 1 other fieldsHigh correlation
Pip113 is highly overall correlated with CYCCH3High correlation
Im12345 is highly overall correlated with aCCH3High correlation
Trz11 is highly overall correlated with –CF2–High correlation
Ammonium is highly overall correlated with NCH3 and 2 other fieldsHigh correlation
Guan is highly overall correlated with NCH3High correlation
Phosphonium is highly overall correlated with PCH2High correlation
Sulfonium is highly overall correlated with SCH3 and 1 other fieldsHigh correlation
aCCH3 is highly overall correlated with Im123 and 3 other fieldsHigh correlation
CYCCH3 is highly overall correlated with Pip113High correlation
aNCH3 is highly overall correlated with Im13 and 1 other fieldsHigh correlation
aNCH2 is highly overall correlated with Im13 and 1 other fieldsHigh correlation
cycNHC3 is highly overall correlated with Pyr11 and 1 other fieldsHigh correlation
cycNCH2 is highly overall correlated with Pyr11 and 2 other fieldsHigh correlation
SCH3 is highly overall correlated with Sulfonium and 1 other fieldsHigh correlation
SCH2 is highly overall correlated with Sulfonium and 1 other fieldsHigh correlation
PCH3 is highly overall correlated with PCH2High correlation
PCH2 is highly overall correlated with Phosphonium and 1 other fieldsHigh correlation
NCH2 is highly overall correlated with NCH3 and 2 other fieldsHigh correlation
–CN is highly overall correlated with cycNCH2High correlation
CH is highly overall correlated with –CH3High correlation
–CF3 is highly overall correlated with –CF2– and 3 other fieldsHigh correlation
BF3CF2 is highly overall correlated with –CF2– and 1 other fieldsHigh correlation
N(SO2CF2)2 is highly overall correlated with –CF3High correlation
CF2SO3 is highly overall correlated with –CF2–High correlation
FAP is highly overall correlated with –CF3High correlation
Im1 is highly imbalanced (84.9%)Imbalance
Im12 is highly imbalanced (95.5%)Imbalance
Im123 is highly imbalanced (77.3%)Imbalance
Py1 is highly imbalanced (83.1%)Imbalance
Py13 is highly imbalanced (84.0%)Imbalance
Py14 is highly imbalanced (86.8%)Imbalance
Py135 is highly imbalanced (98.2%)Imbalance
Pyr is highly imbalanced (90.8%)Imbalance
Pyr1 is highly imbalanced (98.2%)Imbalance
Pyr11 is highly imbalanced (54.2%)Imbalance
Pip11 is highly imbalanced (81.4%)Imbalance
Pip113 is highly imbalanced (94.2%)Imbalance
Mor is highly imbalanced (94.2%)Imbalance
Mor11 is highly imbalanced (83.1%)Imbalance
Ox11 is highly imbalanced (89.8%)Imbalance
Im12345 is highly imbalanced (96.8%)Imbalance
Bentrz is highly imbalanced (90.8%)Imbalance
Trz11 is highly imbalanced (89.8%)Imbalance
NH3 is highly imbalanced (76.5%)Imbalance
NH2 is highly imbalanced (84.9%)Imbalance
NH is highly imbalanced (84.0%)Imbalance
dimethyl[(1R,2S,5R)-(-)menthoxymethyl]ammonium is highly imbalanced (84.9%)Imbalance
Guan is highly imbalanced (96.8%)Imbalance
cycGuan is highly imbalanced (95.5%)Imbalance
BThz is highly imbalanced (95.5%)Imbalance
Phosphonium is highly imbalanced (67.3%)Imbalance
Sulfonium is highly imbalanced (87.8%)Imbalance
aCCH2 is highly imbalanced (90.8%)Imbalance
aCCH3 is highly imbalanced (78.9%)Imbalance
CYCCH3 is highly imbalanced (94.2%)Imbalance
cycNHC3 is highly imbalanced (52.6%)Imbalance
cycNCH2 is highly imbalanced (58.5%)Imbalance
SCH3 is highly imbalanced (96.0%)Imbalance
SCH2 is highly imbalanced (96.0%)Imbalance
PCH3 is highly imbalanced (93.6%)Imbalance
PCH2 is highly imbalanced (81.5%)Imbalance
–OH is highly imbalanced (86.1%)Imbalance
–O– is highly imbalanced (75.7%)Imbalance
–CN is highly imbalanced (86.0%)Imbalance
–CH2Cl is highly imbalanced (96.8%)Imbalance
–COOH is highly imbalanced (95.5%)Imbalance
–COO– is highly imbalanced (80.6%)Imbalance
CH is highly imbalanced (83.4%)Imbalance
–CCH is highly imbalanced (95.1%)Imbalance
–CH=CH2 is highly imbalanced (89.8%)Imbalance
–PH is highly imbalanced (89.4%)Imbalance
–furan is highly imbalanced (96.8%)Imbalance
–thiophene is highly imbalanced (94.2%)Imbalance
–PH1235 is highly imbalanced (95.5%)Imbalance
–tert is highly imbalanced (94.2%)Imbalance
HCOO is highly imbalanced (88.8%)Imbalance
NO3 is highly imbalanced (75.8%)Imbalance
–CF3 is highly imbalanced (61.3%)Imbalance
Cl is highly imbalanced (65.3%)Imbalance
Br is highly imbalanced (66.6%)Imbalance
ClO4 is highly imbalanced (90.8%)Imbalance
BF4 is highly imbalanced (55.8%)Imbalance
SO3CF3 is highly imbalanced (78.9%)Imbalance
PF6 is highly imbalanced (61.6%)Imbalance
CO2CF3 is highly imbalanced (86.8%)Imbalance
acetate is highly imbalanced (91.9%)Imbalance
BF3CF3 is highly imbalanced (86.8%)Imbalance
BF3CF2 is highly imbalanced (68.6%)Imbalance
I is highly imbalanced (89.8%)Imbalance
BF3CH2 is highly imbalanced (95.5%)Imbalance
N(SO2CF2)2 is highly imbalanced (86.8%)Imbalance
DCA is highly imbalanced (84.9%)Imbalance
N(SO2F)2 is highly imbalanced (86.8%)Imbalance
CF2SO3 is highly imbalanced (80.6%)Imbalance
CH2SO4 is highly imbalanced (79.7%)Imbalance
MeSO3 is highly imbalanced (83.1%)Imbalance
SO3CH2 is highly imbalanced (90.8%)Imbalance
MeSO4 is highly imbalanced (87.8%)Imbalance
FAP is highly imbalanced (94.2%)Imbalance
CTF3 is highly imbalanced (96.8%)Imbalance
TCM is highly imbalanced (95.5%)Imbalance
TCB is highly imbalanced (96.8%)Imbalance
AlCl4 is highly imbalanced (96.8%)Imbalance
SCN is highly imbalanced (90.8%)Imbalance
tosylate is highly imbalanced (84.0%)Imbalance
N(SO2CH3)2 is highly imbalanced (95.5%)Imbalance
H2PO4 is highly imbalanced (96.8%)Imbalance
benzoate is highly imbalanced (94.2%)Imbalance
N(SO2CF3)(COCF3) is highly imbalanced (88.8%)Imbalance
saccharate is highly imbalanced (90.8%)Imbalance
TFPB is highly imbalanced (93.0%)Imbalance
HSO4 is highly imbalanced (95.5%)Imbalance
acetylacetonate is highly imbalanced (84.0%)Imbalance
AlBr4 is highly imbalanced (93.0%)Imbalance
NCH3 has 498 (83.0%) zerosZeros
–CH3 has 110 (18.3%) zerosZeros
–CH2– has 185 (30.8%) zerosZeros
–CF2– has 540 (90.0%) zerosZeros

Reproduction

Analysis started2023-01-31 16:14:32.579090
Analysis finished2023-01-31 16:15:59.089954
Duration1 minute and 26.51 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Im1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
587 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Length

2023-01-31T17:15:59.376534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:00.297108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1187
98.9%
1 13
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Im12
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:16:01.256399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:01.847530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Im13
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
475 
1.0
125 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 475
79.2%
1.0 125
 
20.8%

Length

2023-01-31T17:16:02.346735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:03.094183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 475
79.2%
1.0 125
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0 1075
59.7%
. 600
33.3%
1 125
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1075
89.6%
1 125
 
10.4%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1075
59.7%
. 600
33.3%
1 125
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1075
59.7%
. 600
33.3%
1 125
 
6.9%

Im123
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
578 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 578
96.3%
1.0 22
 
3.7%

Length

2023-01-31T17:16:03.612645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:04.320444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 578
96.3%
1.0 22
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0 1178
65.4%
. 600
33.3%
1 22
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1178
98.2%
1 22
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1178
65.4%
. 600
33.3%
1 22
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1178
65.4%
. 600
33.3%
1 22
 
1.2%

Py1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
585 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 585
97.5%
1.0 15
 
2.5%

Length

2023-01-31T17:16:04.685205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:05.091248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 585
97.5%
1.0 15
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1185
98.8%
1 15
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Py13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
586 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Length

2023-01-31T17:16:05.483382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:06.218328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1186
98.8%
1 14
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Py14
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
589 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Length

2023-01-31T17:16:07.741426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:08.349283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1189
99.1%
1 11
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Py135
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
599 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 599
99.8%
1.0 1
 
0.2%

Length

2023-01-31T17:16:08.689552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:09.085664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 599
99.8%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1199
66.6%
. 600
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1199
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1199
66.6%
. 600
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1199
66.6%
. 600
33.3%
1 1
 
0.1%

Pyr
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:16:09.703554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:10.404106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Pyr1
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
599 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 599
99.8%
1.0 1
 
0.2%

Length

2023-01-31T17:16:10.812205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:11.299896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 599
99.8%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1199
66.6%
. 600
33.3%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1199
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1199
66.6%
. 600
33.3%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1199
66.6%
. 600
33.3%
1 1
 
0.1%

Pyr11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
542 
1.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 542
90.3%
1.0 58
 
9.7%

Length

2023-01-31T17:16:11.637992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:12.153562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 542
90.3%
1.0 58
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 1142
63.4%
. 600
33.3%
1 58
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1142
95.2%
1 58
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1142
63.4%
. 600
33.3%
1 58
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1142
63.4%
. 600
33.3%
1 58
 
3.2%

Pip11
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
583 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 583
97.2%
1.0 17
 
2.8%

Length

2023-01-31T17:16:12.753354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:13.339211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 583
97.2%
1.0 17
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 1183
65.7%
. 600
33.3%
1 17
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1183
98.6%
1 17
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1183
65.7%
. 600
33.3%
1 17
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1183
65.7%
. 600
33.3%
1 17
 
0.9%

Pip113
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:16:13.661762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:14.051497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Mor
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:16:14.450029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:14.800251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Mor11
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
585 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 585
97.5%
1.0 15
 
2.5%

Length

2023-01-31T17:16:15.045479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:15.342921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 585
97.5%
1.0 15
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1185
98.8%
1 15
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Ox11
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
592 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Length

2023-01-31T17:16:15.585901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:15.908930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1192
99.3%
1 8
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Im12345
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:16:16.257395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:16.644851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Bentrz
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:16:16.969737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:17.344768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Trz11
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
592 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Length

2023-01-31T17:16:17.697060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:18.102368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1192
99.3%
1 8
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

NH3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
577 
1.0
 
23

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 577
96.2%
1.0 23
 
3.8%

Length

2023-01-31T17:16:18.749718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:19.552910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 577
96.2%
1.0 23
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 1177
65.4%
. 600
33.3%
1 23
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1177
98.1%
1 23
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1177
65.4%
. 600
33.3%
1 23
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1177
65.4%
. 600
33.3%
1 23
 
1.3%

NH2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
587 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Length

2023-01-31T17:16:19.937053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:20.369699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1187
98.9%
1 13
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

NH
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
586 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Length

2023-01-31T17:16:20.739229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:21.146306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1186
98.8%
1 14
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Ammonium
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
454 
1.0
146 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 454
75.7%
1.0 146
 
24.3%

Length

2023-01-31T17:16:21.457009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:21.769421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 454
75.7%
1.0 146
 
24.3%

Most occurring characters

ValueCountFrequency (%)
0 1054
58.6%
. 600
33.3%
1 146
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1054
87.8%
1 146
 
12.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1054
58.6%
. 600
33.3%
1 146
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1054
58.6%
. 600
33.3%
1 146
 
8.1%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
587 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Length

2023-01-31T17:16:22.031362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:22.311724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1187
98.9%
1 13
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Guan
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:16:22.532748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:23.020635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

cycGuan
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:16:23.455774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:23.975895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

BThz
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:16:24.526239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:25.155740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Phosphonium
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
564 
1.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 564
94.0%
1.0 36
 
6.0%

Length

2023-01-31T17:16:25.469911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:25.773025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 564
94.0%
1.0 36
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 1164
64.7%
. 600
33.3%
1 36
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1164
97.0%
1 36
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1164
64.7%
. 600
33.3%
1 36
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1164
64.7%
. 600
33.3%
1 36
 
2.0%

Sulfonium
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
590 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 590
98.3%
1.0 10
 
1.7%

Length

2023-01-31T17:16:26.009614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:26.575804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 590
98.3%
1.0 10
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1190
66.1%
. 600
33.3%
1 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1190
99.2%
1 10
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1190
66.1%
. 600
33.3%
1 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1190
66.1%
. 600
33.3%
1 10
 
0.6%

aCCH2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:16:27.135157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:28.168405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

aCCH3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
554 
1.0
 
43
3.0
 
2
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 554
92.3%
1.0 43
 
7.2%
3.0 2
 
0.3%
2.0 1
 
0.2%

Length

2023-01-31T17:16:28.977224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:29.698353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 554
92.3%
1.0 43
 
7.2%
3.0 2
 
0.3%
2.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1154
64.1%
. 600
33.3%
1 43
 
2.4%
3 2
 
0.1%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1154
96.2%
1 43
 
3.6%
3 2
 
0.2%
2 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1154
64.1%
. 600
33.3%
1 43
 
2.4%
3 2
 
0.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1154
64.1%
. 600
33.3%
1 43
 
2.4%
3 2
 
0.1%
2 1
 
0.1%

CYCCH3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:16:30.552233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:31.620080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

aNCH3
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
450 
1.0
139 
2.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 450
75.0%
1.0 139
 
23.2%
2.0 11
 
1.8%

Length

2023-01-31T17:16:32.183683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:32.665113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 450
75.0%
1.0 139
 
23.2%
2.0 11
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1050
58.3%
. 600
33.3%
1 139
 
7.7%
2 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1050
87.5%
1 139
 
11.6%
2 11
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1050
58.3%
. 600
33.3%
1 139
 
7.7%
2 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1050
58.3%
. 600
33.3%
1 139
 
7.7%
2 11
 
0.6%

aNCH2
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
400 
1.0
176 
2.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 400
66.7%
1.0 176
29.3%
2.0 24
 
4.0%

Length

2023-01-31T17:16:33.009124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:34.039176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 400
66.7%
1.0 176
29.3%
2.0 24
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
55.6%
. 600
33.3%
1 176
 
9.8%
2 24
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1000
83.3%
1 176
 
14.7%
2 24
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1000
55.6%
. 600
33.3%
1 176
 
9.8%
2 24
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1000
55.6%
. 600
33.3%
1 176
 
9.8%
2 24
 
1.3%

cycNHC3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
498 
1.0
89 
2.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 498
83.0%
1.0 89
 
14.8%
2.0 13
 
2.2%

Length

2023-01-31T17:16:34.725782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:35.684330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498
83.0%
1.0 89
 
14.8%
2.0 13
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1098
61.0%
. 600
33.3%
1 89
 
4.9%
2 13
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1098
91.5%
1 89
 
7.4%
2 13
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1098
61.0%
. 600
33.3%
1 89
 
4.9%
2 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1098
61.0%
. 600
33.3%
1 89
 
4.9%
2 13
 
0.7%

cycNCH2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
508 
1.0
88 
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 508
84.7%
1.0 88
 
14.7%
2.0 4
 
0.7%

Length

2023-01-31T17:16:36.005533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:36.798187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 508
84.7%
1.0 88
 
14.7%
2.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1108
61.6%
. 600
33.3%
1 88
 
4.9%
2 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1108
92.3%
1 88
 
7.3%
2 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1108
61.6%
. 600
33.3%
1 88
 
4.9%
2 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1108
61.6%
. 600
33.3%
1 88
 
4.9%
2 4
 
0.2%

SCH3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
595 
3.0
 
3
2.0
 
1
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 595
99.2%
3.0 3
 
0.5%
2.0 1
 
0.2%
1.0 1
 
0.2%

Length

2023-01-31T17:16:37.275485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:37.976177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 595
99.2%
3.0 3
 
0.5%
2.0 1
 
0.2%
1.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
3 3
 
0.2%
2 1
 
0.1%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1195
99.6%
3 3
 
0.2%
2 1
 
0.1%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
3 3
 
0.2%
2 1
 
0.1%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
3 3
 
0.2%
2 1
 
0.1%
1 1
 
0.1%

SCH2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
595 
3.0
 
3
1.0
 
1
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 595
99.2%
3.0 3
 
0.5%
1.0 1
 
0.2%
2.0 1
 
0.2%

Length

2023-01-31T17:16:38.719635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:39.371719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 595
99.2%
3.0 3
 
0.5%
1.0 1
 
0.2%
2.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
3 3
 
0.2%
1 1
 
0.1%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1195
99.6%
3 3
 
0.2%
1 1
 
0.1%
2 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
3 3
 
0.2%
1 1
 
0.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
3 3
 
0.2%
1 1
 
0.1%
2 1
 
0.1%

PCH3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
3.0
 
5
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
3.0 5
 
0.8%
1.0 2
 
0.3%

Length

2023-01-31T17:16:39.770519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:40.257204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
3.0 5
 
0.8%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
3 5
 
0.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
3 5
 
0.4%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
3 5
 
0.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
3 5
 
0.3%
1 2
 
0.1%

PCH2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
566 
4.0
 
26
1.0
 
6
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 566
94.3%
4.0 26
 
4.3%
1.0 6
 
1.0%
3.0 2
 
0.3%

Length

2023-01-31T17:16:40.875920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:41.814651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 566
94.3%
4.0 26
 
4.3%
1.0 6
 
1.0%
3.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1166
64.8%
. 600
33.3%
4 26
 
1.4%
1 6
 
0.3%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1166
97.2%
4 26
 
2.2%
1 6
 
0.5%
3 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1166
64.8%
. 600
33.3%
4 26
 
1.4%
1 6
 
0.3%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1166
64.8%
. 600
33.3%
4 26
 
1.4%
1 6
 
0.3%
3 2
 
0.1%

NCH3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34
Minimum0
Maximum5
Zeros498
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-01-31T17:16:42.442841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.82595622
Coefficient of variation (CV)2.429283
Kurtosis5.2883574
Mean0.34
Median Absolute Deviation (MAD)0
Skewness2.4542815
Sum204
Variance0.68220367
MonotonicityNot monotonic
2023-01-31T17:16:43.077985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 498
83.0%
2 43
 
7.2%
1 31
 
5.2%
3 26
 
4.3%
5 1
 
0.2%
4 1
 
0.2%
ValueCountFrequency (%)
0 498
83.0%
1 31
 
5.2%
2 43
 
7.2%
3 26
 
4.3%
4 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 1
 
0.2%
3 26
 
4.3%
2 43
 
7.2%
1 31
 
5.2%
0 498
83.0%

NCH2
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
405 
4.0
64 
1.0
51 
2.0
43 
3.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 405
67.5%
4.0 64
 
10.7%
1.0 51
 
8.5%
2.0 43
 
7.2%
3.0 37
 
6.2%

Length

2023-01-31T17:16:43.582436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:44.206102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 405
67.5%
4.0 64
 
10.7%
1.0 51
 
8.5%
2.0 43
 
7.2%
3.0 37
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 1005
55.8%
. 600
33.3%
4 64
 
3.6%
1 51
 
2.8%
2 43
 
2.4%
3 37
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1005
83.8%
4 64
 
5.3%
1 51
 
4.2%
2 43
 
3.6%
3 37
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1005
55.8%
. 600
33.3%
4 64
 
3.6%
1 51
 
2.8%
2 43
 
2.4%
3 37
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1005
55.8%
. 600
33.3%
4 64
 
3.6%
1 51
 
2.8%
2 43
 
2.4%
3 37
 
2.1%

–CH3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.615
Minimum0
Maximum7
Zeros110
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-01-31T17:16:44.752552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3795943
Coefficient of variation (CV)0.85423796
Kurtosis-0.075685604
Mean1.615
Median Absolute Deviation (MAD)1
Skewness0.8867242
Sum969
Variance1.9032805
MonotonicityNot monotonic
2023-01-31T17:16:45.495436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 270
45.0%
0 110
18.3%
4 93
 
15.5%
2 73
 
12.2%
3 47
 
7.8%
5 3
 
0.5%
6 3
 
0.5%
7 1
 
0.2%
ValueCountFrequency (%)
0 110
18.3%
1 270
45.0%
2 73
 
12.2%
3 47
 
7.8%
4 93
 
15.5%
5 3
 
0.5%
6 3
 
0.5%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 3
 
0.5%
5 3
 
0.5%
4 93
 
15.5%
3 47
 
7.8%
2 73
 
12.2%
1 270
45.0%
0 110
18.3%

–CH2–
Real number (ℝ)

Distinct25
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6883333
Minimum0
Maximum30
Zeros185
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-01-31T17:16:45.995384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile16
Maximum30
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2243041
Coefficient of variation (CV)1.4164403
Kurtosis3.7507222
Mean3.6883333
Median Absolute Deviation (MAD)2
Skewness1.984297
Sum2213
Variance27.293353
MonotonicityNot monotonic
2023-01-31T17:16:46.433803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 185
30.8%
2 109
18.2%
1 98
16.3%
4 41
 
6.8%
6 24
 
4.0%
3 22
 
3.7%
8 21
 
3.5%
10 17
 
2.8%
18 14
 
2.3%
12 14
 
2.3%
Other values (15) 55
 
9.2%
ValueCountFrequency (%)
0 185
30.8%
1 98
16.3%
2 109
18.2%
3 22
 
3.7%
4 41
 
6.8%
5 6
 
1.0%
6 24
 
4.0%
7 7
 
1.2%
8 21
 
3.5%
9 5
 
0.8%
ValueCountFrequency (%)
30 1
 
0.2%
28 1
 
0.2%
24 3
 
0.5%
22 1
 
0.2%
21 1
 
0.2%
20 2
 
0.3%
19 1
 
0.2%
18 14
2.3%
17 4
 
0.7%
16 13
2.2%

–OH
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
580 
1.0
 
19
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 580
96.7%
1.0 19
 
3.2%
2.0 1
 
0.2%

Length

2023-01-31T17:16:46.889714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:48.318925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 580
96.7%
1.0 19
 
3.2%
2.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1180
65.6%
. 600
33.3%
1 19
 
1.1%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1180
98.3%
1 19
 
1.6%
2 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1180
65.6%
. 600
33.3%
1 19
 
1.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1180
65.6%
. 600
33.3%
1 19
 
1.1%
2 1
 
0.1%
Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
551 
1.0
 
37
2.0
 
10
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 551
91.8%
1.0 37
 
6.2%
2.0 10
 
1.7%
4.0 2
 
0.3%

Length

2023-01-31T17:16:48.931647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:49.411258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 551
91.8%
1.0 37
 
6.2%
2.0 10
 
1.7%
4.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1151
63.9%
. 600
33.3%
1 37
 
2.1%
2 10
 
0.6%
4 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1151
95.9%
1 37
 
3.1%
2 10
 
0.8%
4 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1151
63.9%
. 600
33.3%
1 37
 
2.1%
2 10
 
0.6%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1151
63.9%
. 600
33.3%
1 37
 
2.1%
2 10
 
0.6%
4 2
 
0.1%

–CN
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
581 
1.0
 
16
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 581
96.8%
1.0 16
 
2.7%
2.0 3
 
0.5%

Length

2023-01-31T17:16:49.681510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:49.988576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 581
96.8%
1.0 16
 
2.7%
2.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 16
 
0.9%
2 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1181
98.4%
1 16
 
1.3%
2 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 16
 
0.9%
2 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 16
 
0.9%
2 3
 
0.2%

–CH2Cl
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:16:50.251784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:50.636961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

–COOH
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:16:50.939511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:51.414189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
582 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 582
97.0%
1.0 18
 
3.0%

Length

2023-01-31T17:16:51.778513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:52.246651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 582
97.0%
1.0 18
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 1182
65.7%
. 600
33.3%
1 18
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1182
98.5%
1 18
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1182
65.7%
. 600
33.3%
1 18
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1182
65.7%
. 600
33.3%
1 18
 
1.0%

CH
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
572 
1.0
 
15
2.0
 
11
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 572
95.3%
1.0 15
 
2.5%
2.0 11
 
1.8%
3.0 2
 
0.3%

Length

2023-01-31T17:16:52.571078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:53.076889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 572
95.3%
1.0 15
 
2.5%
2.0 11
 
1.8%
3.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1172
65.1%
. 600
33.3%
1 15
 
0.8%
2 11
 
0.6%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1172
97.7%
1 15
 
1.2%
2 11
 
0.9%
3 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1172
65.1%
. 600
33.3%
1 15
 
0.8%
2 11
 
0.6%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1172
65.1%
. 600
33.3%
1 15
 
0.8%
2 11
 
0.6%
3 2
 
0.1%

–CCH
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
595 
2.0
 
3
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 595
99.2%
2.0 3
 
0.5%
1.0 2
 
0.3%

Length

2023-01-31T17:16:53.796697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:54.446353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 595
99.2%
2.0 3
 
0.5%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
2 3
 
0.2%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1195
99.6%
2 3
 
0.2%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
2 3
 
0.2%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
2 3
 
0.2%
1 2
 
0.1%

–CH=CH2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
592 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Length

2023-01-31T17:16:54.900589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:55.359659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1192
99.3%
1 8
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

–PH
Categorical

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
581 
1.0
 
13
3.0
 
3
4.0
 
2
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 581
96.8%
1.0 13
 
2.2%
3.0 3
 
0.5%
4.0 2
 
0.3%
2.0 1
 
0.2%

Length

2023-01-31T17:16:55.867477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:56.263986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 581
96.8%
1.0 13
 
2.2%
3.0 3
 
0.5%
4.0 2
 
0.3%
2.0 1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 13
 
0.7%
3 3
 
0.2%
4 2
 
0.1%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1181
98.4%
1 13
 
1.1%
3 3
 
0.2%
4 2
 
0.2%
2 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 13
 
0.7%
3 3
 
0.2%
4 2
 
0.1%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 13
 
0.7%
3 3
 
0.2%
4 2
 
0.1%
2 1
 
0.1%

–furan
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:16:56.576864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:57.072901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:16:57.454657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:57.787269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

–PH1235
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:16:58.034064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:58.414933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

–tert
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:16:58.738486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:59.030117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

HCOO
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
591 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 591
98.5%
1.0 9
 
1.5%

Length

2023-01-31T17:16:59.391013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:16:59.997571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 591
98.5%
1.0 9
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1191
66.2%
. 600
33.3%
1 9
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1191
99.2%
1 9
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1191
66.2%
. 600
33.3%
1 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1191
66.2%
. 600
33.3%
1 9
 
0.5%

NO3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
576 
1.0
 
24

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 576
96.0%
1.0 24
 
4.0%

Length

2023-01-31T17:17:00.376254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:00.690145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 576
96.0%
1.0 24
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 1176
65.3%
. 600
33.3%
1 24
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1176
98.0%
1 24
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1176
65.3%
. 600
33.3%
1 24
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1176
65.3%
. 600
33.3%
1 24
 
1.3%

–CF3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
503 
1.0
79 
2.0
 
14
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 503
83.8%
1.0 79
 
13.2%
2.0 14
 
2.3%
3.0 4
 
0.7%

Length

2023-01-31T17:17:01.021435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:01.411696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 503
83.8%
1.0 79
 
13.2%
2.0 14
 
2.3%
3.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1103
61.3%
. 600
33.3%
1 79
 
4.4%
2 14
 
0.8%
3 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1103
91.9%
1 79
 
6.6%
2 14
 
1.2%
3 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1103
61.3%
. 600
33.3%
1 79
 
4.4%
2 14
 
0.8%
3 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1103
61.3%
. 600
33.3%
1 79
 
4.4%
2 14
 
0.8%
3 4
 
0.2%

–CF2–
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23166667
Minimum0
Maximum6
Zeros540
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-01-31T17:17:01.698591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80357957
Coefficient of variation (CV)3.4686888
Kurtosis20.734555
Mean0.23166667
Median Absolute Deviation (MAD)0
Skewness4.2632984
Sum139
Variance0.64574012
MonotonicityNot monotonic
2023-01-31T17:17:01.929129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 540
90.0%
2 30
 
5.0%
1 14
 
2.3%
3 7
 
1.2%
4 4
 
0.7%
6 3
 
0.5%
5 2
 
0.3%
ValueCountFrequency (%)
0 540
90.0%
1 14
 
2.3%
2 30
 
5.0%
3 7
 
1.2%
4 4
 
0.7%
5 2
 
0.3%
6 3
 
0.5%
ValueCountFrequency (%)
6 3
 
0.5%
5 2
 
0.3%
4 4
 
0.7%
3 7
 
1.2%
2 30
 
5.0%
1 14
 
2.3%
0 540
90.0%

Cl
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
561 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 561
93.5%
1.0 39
 
6.5%

Length

2023-01-31T17:17:02.536207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:02.835999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 561
93.5%
1.0 39
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 1161
64.5%
. 600
33.3%
1 39
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1161
96.8%
1 39
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1161
64.5%
. 600
33.3%
1 39
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1161
64.5%
. 600
33.3%
1 39
 
2.2%

Br
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
563 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 563
93.8%
1.0 37
 
6.2%

Length

2023-01-31T17:17:03.039287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:03.326887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 563
93.8%
1.0 37
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 1163
64.6%
. 600
33.3%
1 37
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1163
96.9%
1 37
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1163
64.6%
. 600
33.3%
1 37
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1163
64.6%
. 600
33.3%
1 37
 
2.1%

ClO4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:17:03.561069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:03.874451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

BF4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
545 
1.0
55 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 545
90.8%
1.0 55
 
9.2%

Length

2023-01-31T17:17:04.077924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:04.336806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 545
90.8%
1.0 55
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 1145
63.6%
. 600
33.3%
1 55
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1145
95.4%
1 55
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1145
63.6%
. 600
33.3%
1 55
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1145
63.6%
. 600
33.3%
1 55
 
3.1%

tf2n
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
498 
1.0
102 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 498
83.0%
1.0 102
 
17.0%

Length

2023-01-31T17:17:04.534578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:04.913808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 498
83.0%
1.0 102
 
17.0%

Most occurring characters

ValueCountFrequency (%)
0 1098
61.0%
. 600
33.3%
1 102
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1098
91.5%
1 102
 
8.5%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1098
61.0%
. 600
33.3%
1 102
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1098
61.0%
. 600
33.3%
1 102
 
5.7%

SO3CF3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
580 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 580
96.7%
1.0 20
 
3.3%

Length

2023-01-31T17:17:05.220988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:05.681478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 580
96.7%
1.0 20
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 1180
65.6%
. 600
33.3%
1 20
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1180
98.3%
1 20
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1180
65.6%
. 600
33.3%
1 20
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1180
65.6%
. 600
33.3%
1 20
 
1.1%

PF6
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
555 
1.0
 
45

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 555
92.5%
1.0 45
 
7.5%

Length

2023-01-31T17:17:05.954595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:06.226225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 555
92.5%
1.0 45
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 1155
64.2%
. 600
33.3%
1 45
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1155
96.2%
1 45
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1155
64.2%
. 600
33.3%
1 45
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1155
64.2%
. 600
33.3%
1 45
 
2.5%

CO2CF3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
589 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Length

2023-01-31T17:17:06.428610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:06.694170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1189
99.1%
1 11
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

acetate
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
594 
1.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 594
99.0%
1.0 6
 
1.0%

Length

2023-01-31T17:17:06.941019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:07.247484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 594
99.0%
1.0 6
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1194
66.3%
. 600
33.3%
1 6
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1194
99.5%
1 6
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1194
66.3%
. 600
33.3%
1 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1194
66.3%
. 600
33.3%
1 6
 
0.3%

BF3CF3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
589 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Length

2023-01-31T17:17:07.485677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:07.751345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1189
99.1%
1 11
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

BF3CF2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
566 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 566
94.3%
1.0 34
 
5.7%

Length

2023-01-31T17:17:07.989418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:08.296333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 566
94.3%
1.0 34
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 1166
64.8%
. 600
33.3%
1 34
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1166
97.2%
1 34
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1166
64.8%
. 600
33.3%
1 34
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1166
64.8%
. 600
33.3%
1 34
 
1.9%

I
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
592 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Length

2023-01-31T17:17:08.895347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:09.392133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 592
98.7%
1.0 8
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1192
99.3%
1 8
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1192
66.2%
. 600
33.3%
1 8
 
0.4%

BF3CH2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:17:09.651496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:10.006890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

N(SO2CF2)2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
589 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Length

2023-01-31T17:17:10.466686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:10.829612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1189
99.1%
1 11
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

DCA
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
587 
1.0
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Length

2023-01-31T17:17:11.061302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:11.372522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 587
97.8%
1.0 13
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1187
98.9%
1 13
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1187
65.9%
. 600
33.3%
1 13
 
0.7%

N(SO2F)2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
589 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Length

2023-01-31T17:17:11.591343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:11.862245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 589
98.2%
1.0 11
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1189
99.1%
1 11
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1189
66.1%
. 600
33.3%
1 11
 
0.6%

CF2SO3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
582 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 582
97.0%
1.0 18
 
3.0%

Length

2023-01-31T17:17:12.052562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:12.334881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 582
97.0%
1.0 18
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 1182
65.7%
. 600
33.3%
1 18
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1182
98.5%
1 18
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1182
65.7%
. 600
33.3%
1 18
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1182
65.7%
. 600
33.3%
1 18
 
1.0%

CH2SO4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
581 
1.0
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 581
96.8%
1.0 19
 
3.2%

Length

2023-01-31T17:17:12.574716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:12.842990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 581
96.8%
1.0 19
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 19
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1181
98.4%
1 19
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 19
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1181
65.6%
. 600
33.3%
1 19
 
1.1%

MeSO3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
585 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 585
97.5%
1.0 15
 
2.5%

Length

2023-01-31T17:17:13.074930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:13.354962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 585
97.5%
1.0 15
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1185
98.8%
1 15
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1185
65.8%
. 600
33.3%
1 15
 
0.8%

SO3CH2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:17:13.561333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:13.845120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

MeSO4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
590 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 590
98.3%
1.0 10
 
1.7%

Length

2023-01-31T17:17:14.054960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:14.345873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 590
98.3%
1.0 10
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 1190
66.1%
. 600
33.3%
1 10
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1190
99.2%
1 10
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1190
66.1%
. 600
33.3%
1 10
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1190
66.1%
. 600
33.3%
1 10
 
0.6%

FAP
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:17:14.562440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:14.839073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

CTF3
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:17:15.054723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:15.329614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

TCM
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:17:15.656247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:15.962350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

TCB
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:17:16.195701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:16.479785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

AlCl4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:17:16.699880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:16.984460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

SCN
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:17:17.189440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:17.467846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

tosylate
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
586 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Length

2023-01-31T17:17:17.730471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:17.991497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1186
98.8%
1 14
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

N(SO2CH3)2
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:17:18.210608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:18.510711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

H2PO4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
598 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Length

2023-01-31T17:17:18.806439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:19.106934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598
99.7%
1.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1198
99.8%
1 2
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1198
66.6%
. 600
33.3%
1 2
 
0.1%

benzoate
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
596 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Length

2023-01-31T17:17:19.331413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:20.073196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596
99.3%
1.0 4
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1196
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1196
66.4%
. 600
33.3%
1 4
 
0.2%

N(SO2CF3)(COCF3)
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
591 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 591
98.5%
1.0 9
 
1.5%

Length

2023-01-31T17:17:20.354000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:20.797332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 591
98.5%
1.0 9
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1191
66.2%
. 600
33.3%
1 9
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1191
99.2%
1 9
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1191
66.2%
. 600
33.3%
1 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1191
66.2%
. 600
33.3%
1 9
 
0.5%

saccharate
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
593 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Length

2023-01-31T17:17:21.117225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:21.502808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 593
98.8%
1.0 7
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1193
99.4%
1 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1193
66.3%
. 600
33.3%
1 7
 
0.4%

TFPB
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
595 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 595
99.2%
1.0 5
 
0.8%

Length

2023-01-31T17:17:21.909708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:22.352776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 595
99.2%
1.0 5
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1195
99.6%
1 5
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
1 5
 
0.3%

HSO4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
597 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Length

2023-01-31T17:17:22.702210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:23.226769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 597
99.5%
1.0 3
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1197
99.8%
1 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1197
66.5%
. 600
33.3%
1 3
 
0.2%

acetylacetonate
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
586 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Length

2023-01-31T17:17:23.508042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:23.823066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 586
97.7%
1.0 14
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1186
98.8%
1 14
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1186
65.9%
. 600
33.3%
1 14
 
0.8%

AlBr4
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
0.0
595 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1800
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 595
99.2%
1.0 5
 
0.8%

Length

2023-01-31T17:17:24.091617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-31T17:17:24.383709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 595
99.2%
1.0 5
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
1 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
66.7%
Other Punctuation 600
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1195
99.6%
1 5
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
1 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1195
66.4%
. 600
33.3%
1 5
 
0.3%

exp (K)
Real number (ℝ)

Distinct206
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.28633
Minimum177.15
Maximum550.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.4 KiB
2023-01-31T17:17:24.646792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum177.15
5-th percentile243.15
Q1284.15
median318.15
Q3355.15
95-th percentile423.15
Maximum550.15
Range373
Interquartile range (IQR)71

Descriptive statistics

Standard deviation54.372142
Coefficient of variation (CV)0.16870756
Kurtosis0.61290839
Mean322.28633
Median Absolute Deviation (MAD)35
Skewness0.49378948
Sum193371.8
Variance2956.3299
MonotonicityNot monotonic
2023-01-31T17:17:25.062804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
313.15 10
 
1.7%
311.15 10
 
1.7%
344.15 8
 
1.3%
327.15 8
 
1.3%
299.15 8
 
1.3%
345.15 7
 
1.2%
294.15 7
 
1.2%
323.15 7
 
1.2%
351.15 7
 
1.2%
318.15 7
 
1.2%
Other values (196) 521
86.8%
ValueCountFrequency (%)
177.15 1
 
0.2%
187.15 1
 
0.2%
190.15 1
 
0.2%
191.15 1
 
0.2%
192.15 1
 
0.2%
193.15 1
 
0.2%
205.15 1
 
0.2%
212.15 1
 
0.2%
215.15 1
 
0.2%
218.15 3
0.5%
ValueCountFrequency (%)
550.15 1
0.2%
490.15 1
0.2%
486.15 1
0.2%
478.15 1
0.2%
477.15 1
0.2%
473.15 1
0.2%
471.15 1
0.2%
467.15 1
0.2%
465.15 1
0.2%
462.15 1
0.2%

Interactions

2023-01-31T17:15:49.869503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:38.322469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:39.898293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:41.503208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:47.497382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:50.418034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:38.570362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:40.270329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:42.236968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:48.016984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:50.764476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:38.856431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:40.597178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:43.800095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:48.345443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:51.033407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:39.131343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:40.895360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:44.569758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:48.735981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:51.437036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:39.474932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:41.160560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:46.178864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-01-31T17:15:49.334004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-01-31T17:17:25.975625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
NCH3–CH3–CH2––CF2–exp (K)Im1Im12Im13Im123Py1Py13Py14Py135PyrPyr1Pyr11Pip11Pip113MorMor11Ox11Im12345BentrzTrz11NH3NH2NHAmmoniumdimethyl[(1R,2S,5R)-(-)menthoxymethyl]ammoniumGuancycGuanBThzPhosphoniumSulfoniumaCCH2aCCH3CYCCH3aNCH3aNCH2cycNHC3cycNCH2SCH3SCH2PCH3PCH2NCH2–OH–O––CN–CH2Cl–COOH–COO–CH–CCH–CH=CH2–PH–furan–thiophene–PH1235–tertHCOONO3–CF3ClBrClO4BF4tf2nSO3CF3PF6CO2CF3acetateBF3CF3BF3CF2IBF3CH2N(SO2CF2)2DCAN(SO2F)2CF2SO3CH2SO4MeSO3SO3CH2MeSO4FAPCTF3TCMTCBAlCl4SCNtosylateN(SO2CH3)2H2PO4benzoateN(SO2CF3)(COCF3)saccharateTFPBHSO4acetylacetonateAlBr4
NCH31.0000.0350.022-0.0470.0690.0000.0000.2140.0000.0000.0000.0000.0000.0000.0000.1170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5830.4860.9970.2900.0000.0690.0000.0000.0000.0000.1610.2070.1330.1010.0000.0000.0000.0000.5800.0940.1930.0000.0310.0470.0000.0310.0250.0000.0890.0000.0000.0000.0000.0000.1860.0000.0090.0550.0000.2550.1080.0000.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.1540.0000.0700.0000.0000.1050.0000.101
–CH30.0351.0000.423-0.070-0.0940.2010.1040.2820.1220.0890.1040.0280.0000.2020.0000.2020.0720.0000.1350.0000.0700.0570.0420.0000.0810.2080.4110.5630.0180.0000.0000.0000.3660.1250.0000.1000.0000.3070.4070.2660.2340.0220.1130.1030.2810.5940.1430.1760.2100.0000.1040.2030.5230.0840.0120.0480.0140.2720.0700.2760.0000.0190.0510.1130.1800.0000.0920.0000.0810.0910.0000.0000.0000.1190.0370.1570.0000.0000.0580.0000.1390.0000.2710.0810.0000.0810.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.3140.063
–CH2–0.0220.4231.000-0.0650.0420.0000.0000.1770.0710.0240.0540.0000.0000.0000.0000.1260.0000.0000.0000.0000.0000.0000.0000.0000.0680.0000.0000.2350.0720.0000.0000.0360.2260.0000.1410.0000.0000.0730.1760.1240.1030.0000.0000.0000.1550.1800.0000.1490.0000.0000.0000.0000.1480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1870.2020.1500.1040.0000.0000.1690.0000.0000.0000.1170.1830.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.1120.0000.0000.000
–CF2–-0.047-0.070-0.0651.000-0.0080.0000.0000.0000.0000.0000.1160.0000.0000.2680.0000.1060.0720.0000.0000.0000.1510.0000.0000.5540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1380.1040.1040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3430.0000.0000.5210.0000.0000.0000.0350.1600.0000.0000.0000.0000.0000.5970.0000.0000.4370.0000.0000.7210.4460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1370.000
exp (K)0.069-0.0940.042-0.0081.0000.0000.0000.2140.1440.0000.0770.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.3370.0970.0000.0000.0000.3520.1110.1160.0000.0000.0000.1430.1220.2180.1970.0000.1240.1060.0960.0000.1600.0000.0000.0000.1250.0000.0900.0850.0000.1210.0000.2920.0000.0390.3960.2190.1990.0000.3340.0240.0000.1030.1990.3390.1110.1340.2060.0000.1230.0000.0000.1230.0000.2160.0000.0000.2500.1600.1150.2530.0960.0000.0590.1280.0000.0500.0000.0000.0000.0930.1510.0590.0000.0290.0000.0160.0650.3630.000
Im10.0000.2010.0000.0000.0001.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.1810.0000.0350.0260.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.1380.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Im120.0000.1040.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2450.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.4250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Im130.2140.2820.1770.0000.2140.0470.0001.0000.0790.0560.0510.0370.0000.0000.0000.1560.0630.0000.0000.0560.0090.0000.0000.0090.0820.0470.0510.2830.0470.0000.0000.0000.1140.0300.0000.1300.0000.7260.6540.2250.2110.0000.0000.0000.1040.3470.0760.0910.0000.0000.0000.0670.0430.1330.0000.0440.0000.0000.1010.0000.0220.0610.0380.0000.0590.0000.0000.0480.0900.0500.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0520.0000.0510.000
Im1230.0000.1220.0710.0000.1440.0000.0000.0791.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.0000.0000.0000.2650.5610.0000.2720.2520.0670.0600.0000.0000.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.0000.0840.0480.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Py10.0000.0890.0240.0000.0000.0000.0000.0560.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.2420.0440.0360.0000.0000.0000.0000.0750.0000.0000.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0160.0000.0000.000
Py130.0000.1040.0540.1160.0770.0000.0000.0510.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.5090.0000.0280.2080.0390.0310.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0490.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.000
Py140.0000.0280.0000.0000.0000.0000.0000.0370.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.1530.3890.0000.0000.1760.0220.0070.0000.0000.0000.0000.0480.0000.0000.2010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0680.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0510.0000.0000.0000.0000.0000.000
Py1350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9980.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Pyr0.0000.2020.0000.2680.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.2040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.000
Pyr10.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Pyr110.1170.2020.1260.1060.0770.0000.0000.1560.0270.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.1740.0000.0000.0000.0000.0580.0000.0000.0620.0000.1800.2240.6770.6440.0000.0000.0000.0380.2120.0410.0150.1350.0000.0000.0030.0150.0000.0000.0000.0000.0000.0000.0000.0000.0330.1710.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.1390.1190.0000.0820.0000.1020.0000.0000.0660.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0000.0000.0000.000
Pip110.0000.0720.0000.0720.0000.0000.0000.0630.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.1060.3540.3710.0000.0000.0000.0000.0860.0610.1030.1530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Pip1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8740.0000.0040.1880.1890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1510.0000.0510.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Mor0.0000.1350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.1120.0000.1120.0000.0000.1870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4610.000
Mor110.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.0720.0970.3560.3520.0000.0000.0000.0000.0750.0730.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Ox110.0000.0700.0000.1510.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0340.0590.2730.2750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Im123450.0000.0570.0000.0000.3370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9980.0000.4200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Bentrz0.0000.0420.0000.0000.0970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.2290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.000
Trz110.0000.0000.0000.5540.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.1090.2060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1060.0000.0000.0000.0000.1550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
NH30.0000.0810.0680.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0950.0000.0000.0000.0000.0000.0000.0000.0000.0000.1000.1290.0700.0620.0000.0000.0000.0000.3690.0970.0000.0000.0000.1660.3960.4440.0000.0000.0000.0000.0000.0000.0000.2940.3340.0520.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0240.0000.000
NH20.0000.2080.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0880.0350.0260.0000.0000.0000.0000.3960.2700.0000.0000.0000.0000.0000.1430.0000.0000.0000.0000.1950.0000.0410.0000.0000.1200.0000.0000.0000.0000.0320.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3160.000
NH0.0000.4110.0000.0000.3520.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0930.0390.0310.0000.0000.0000.0000.4590.0000.0000.0000.2760.0000.0000.3690.0000.0000.0000.0770.0370.0000.1870.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.3030.000
Ammonium0.5830.5630.2350.0000.1110.0580.0000.2830.0920.0670.0630.0480.0000.0150.0000.1740.0750.0000.0000.0670.0270.0000.0150.0270.0950.0580.0631.0000.0580.0000.0000.0000.1290.0420.0150.1470.0000.3230.3970.2500.2350.0000.0000.0210.1200.8320.0530.1330.0570.0000.0000.0580.0830.0000.0000.1080.0000.0000.0000.0000.0000.0000.0000.1020.0790.0000.0000.0420.0310.0690.0000.0000.0330.0000.0760.0000.0000.0000.0000.0130.0080.0350.0150.0000.0000.0550.0000.0000.0000.0000.0000.0000.0550.0000.0090.0000.0000.0000.0630.089
dimethyl[(1R,2S,5R)-(-)menthoxymethyl]ammonium0.4860.0180.0720.0000.1160.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0581.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0880.0350.0260.0000.0000.0000.0000.4000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3670.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Guan0.9970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.2070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
cycGuan0.2900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.4730.0000.0000.0000.0000.0000.2180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
BThz0.0000.0000.0360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Phosphonium0.0690.3660.2260.0000.1430.0000.0000.1140.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0000.0001.0000.0000.0000.0170.0000.1340.1690.0990.0910.0000.0000.4260.9680.1550.0000.0000.0000.0000.0000.0000.0000.0000.0470.2320.0000.0000.0000.0000.0000.0000.0860.3150.0000.0000.0550.0480.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Sulfonium0.0000.1250.0000.0000.1220.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0001.0000.0000.0000.0000.0480.0720.0120.0000.7010.7010.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0000.0000.3510.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.000
aCCH20.0000.0000.1410.0000.2180.0000.0000.0000.2650.0000.0000.1530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0001.0000.0000.0000.0660.1090.0000.0000.0000.0000.0000.0000.0000.0000.0000.1650.0000.0000.0000.0440.0000.0370.1800.0000.0000.0000.0000.0000.0000.0000.0000.2600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
aCCH30.0000.1000.0000.0000.1970.0000.2450.1300.5610.0000.5090.3890.9980.0000.0000.0620.0000.0000.0000.0000.0000.9980.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0170.0000.0001.0000.0000.2990.2650.0590.0500.0000.0000.0000.0000.0820.0000.0000.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.1490.0000.0000.0330.0610.1240.0000.0000.0000.0000.2370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.000
CYCCH30.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.8740.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0040.1880.1890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1510.0000.0510.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
aNCH30.1610.3070.0730.0000.1240.1810.0000.7260.2720.0720.0280.0000.0000.0240.0000.1800.0800.0000.0000.0720.0340.4200.0660.1090.1000.0640.0680.3230.0640.0000.0000.0000.1340.0480.0660.2990.0001.0000.5220.1760.1640.0000.0000.0000.0710.2720.0490.0480.0000.0000.0000.0840.0000.0000.0000.0000.0000.0000.0000.0000.0420.0770.0000.0000.0000.0000.0000.0390.1040.0860.0500.0000.0470.0640.0760.1160.0000.0000.0000.0000.0230.0000.1890.0640.0000.0000.0000.0000.0880.0240.0000.0000.0000.0000.0000.0000.0560.0000.0680.000
aNCH20.2070.4070.1760.1380.1060.0000.0940.6540.2520.2420.2080.1760.0260.0510.0000.2240.1060.0040.0040.0970.0590.0000.2290.2060.1290.0880.0930.3970.0880.0000.0000.0940.1690.0720.1090.2650.0040.5221.0000.2190.2050.0000.0000.0000.1000.3380.0730.2260.1250.0000.0000.1100.0480.2400.0000.0000.0000.0040.0090.0040.0650.1130.0000.0000.0000.0000.1380.0190.1140.2070.0000.0410.0000.0810.0000.0940.0000.0000.0000.0000.0130.0000.1590.0300.0000.0000.0000.0000.0690.0510.0000.0000.0000.0000.0110.0000.0840.0000.0930.029
cycNHC30.1330.2660.1240.1040.0960.0350.0000.2250.0670.0440.0390.0220.0000.0000.0790.6770.3540.1880.0000.3560.2730.0000.0000.0000.0700.0350.0390.2500.0350.0000.4730.0000.0990.0120.0000.0590.1880.1760.2191.0000.7010.0000.0000.0000.0340.2040.0810.1050.0000.0000.0000.0550.0020.0000.0000.0000.0000.0000.0000.0000.0000.0320.1140.0360.0000.0700.0000.0000.0000.0310.0000.0000.0000.2370.0000.0000.0920.0690.0000.0000.0000.0480.0000.2420.0570.0000.0000.0000.0000.0000.0000.1800.0000.0000.0680.0000.0000.0000.0390.000
cycNCH20.1010.2340.1030.1040.0000.0260.0000.2110.0600.0360.0310.0070.0000.0000.0000.6440.3710.1890.0000.3520.2750.0000.0000.0000.0620.0260.0310.2350.0260.0000.0000.0000.0910.0000.0000.0500.1890.1640.2050.7011.0000.0000.0000.0000.0210.1920.0820.1040.6100.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.1110.0000.0380.0000.0000.0550.0000.0380.0000.0000.0000.2380.0000.0000.0610.0370.0000.0000.0850.0000.0000.0000.0580.0000.0000.0020.0000.0000.0000.0870.0000.0000.0000.0000.0000.0000.0310.000
SCH30.0000.0220.0000.0000.1600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7010.0000.0000.0000.0000.0000.0000.0001.0000.8150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0000.0000.2660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.000
SCH20.0000.1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.7010.0000.0000.0000.0000.0000.0000.0000.8151.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1110.0000.0000.0000.0000.0000.0000.0000.0000.0000.2660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.000
PCH30.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.4260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.9560.0000.0000.0590.0000.0000.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PCH20.0000.2810.1550.0000.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1200.0000.0000.0000.0000.9680.0000.0000.0000.0000.0710.1000.0340.0210.0000.0000.9561.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.1250.1080.0000.0000.0000.0000.0000.0000.0000.3520.0000.0000.0320.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1850.0000.0000.0000.0000.0000.007
NCH20.5800.5940.1800.0000.1250.0630.0000.3470.1080.0750.0700.0480.0000.0000.0000.2120.0860.0000.0000.0750.0000.0000.0000.0000.3690.3960.4590.8320.4000.1070.2180.0000.1550.0390.0000.0820.0000.2720.3380.2040.1920.0000.0000.0000.0551.0000.1350.0980.0000.1160.2180.0000.1850.0000.0000.0140.0690.0420.0000.1430.2010.1120.0520.1210.1250.0890.1250.0000.0760.0800.0690.0920.0000.0620.1310.0000.0000.0190.0000.0250.0630.0000.0000.0660.0000.1460.0000.0000.0000.0000.0000.0000.1160.0000.0310.0770.0000.0690.2250.113
–OH0.0940.1430.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0410.0610.0000.0000.0730.0000.0000.0000.0000.0970.2700.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0730.0810.0820.0000.0000.0000.0000.1351.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0380.0960.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1440.0000.0000.0000.0000.0000.0000.000
–O–0.1930.1760.1490.0000.0900.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0150.1030.0000.0000.0620.0000.0000.0000.0000.0000.0000.0000.1330.0000.2070.0000.0000.0000.0000.0000.0000.0000.0480.2260.1050.1040.0000.0000.0590.0000.0980.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.1050.0290.0000.2240.0000.0000.0000.0000.1060.3190.3200.0000.0000.0000.0000.2240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
–CN0.0000.2100.0000.0000.0850.0000.4250.0000.0000.0900.0000.2010.0000.0000.0000.1350.1530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0000.0000.0000.0000.0000.0000.1650.0850.0000.0000.1250.0000.6100.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1820.0000.0130.0740.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
–CH2Cl0.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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–COO–0.0000.2030.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.3960.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.1100.0550.0480.0000.0000.0000.0000.0000.0550.0000.0000.0000.0001.0000.4530.0000.0000.0000.0000.0000.0000.0000.0000.2860.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
CH0.0310.5230.1480.0000.2920.0000.0000.0430.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.4440.1430.3690.0830.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0480.0020.0000.0000.0000.0000.0000.1850.0000.0000.0000.0000.0000.4531.0000.0000.0000.0000.0000.3730.0000.3730.1400.2870.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0000.0000.0000.3960.000
–CCH0.0250.0840.0000.0000.0000.0000.0000.1330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2400.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0530.0000.0000.1300.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.1810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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–PH0.0890.0480.0000.0000.3960.0000.0000.0440.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1800.0000.0000.0000.0000.1080.0000.0000.0000.0000.2320.3510.1800.0000.0000.0000.0000.0000.0000.0000.0000.0000.1080.0140.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0340.0480.0750.1740.0000.0000.0410.0600.0000.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1800.1000.0000.0000.2730.0000.0000.0000.0000.0000.000
–furan0.0000.0140.0000.0000.2190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2760.000
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–PH12350.0000.0700.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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HCOO0.0000.0000.0000.0000.0240.0000.0000.0220.0000.0000.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0650.0000.0000.0000.0000.0000.0000.2010.0000.0000.0000.0000.0000.0000.1400.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
NO30.1860.0190.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.3340.0000.0000.0000.0000.0470.0210.0000.0000.0000.0000.0000.0000.0770.1130.0320.0260.0000.0000.0000.0000.1120.0190.0000.0000.0000.0210.2860.2870.0000.0000.0340.0000.0000.0000.0000.0001.0000.0550.0000.0000.0000.0290.0700.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
–CF30.0000.0510.0000.5210.1030.0310.0000.0380.0000.0000.0000.0000.0000.0650.0000.1710.0890.0000.1870.0000.1060.0000.0000.1060.0520.1200.0210.0000.0000.0000.0000.0000.0860.0410.0000.0000.0000.0000.0000.1140.1110.0000.0000.0000.0000.0520.0000.0850.0000.0000.0000.0310.0650.0000.0000.0480.1310.1980.0000.1980.0000.0551.0000.0920.0880.0000.1200.1100.0410.1030.0000.0000.0000.6260.0000.0000.8820.0000.0000.4460.2290.0000.0000.0000.9980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3330.000
Cl0.0090.1130.1870.0000.1990.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.3150.0000.0000.0860.0000.0000.0000.0360.0000.0000.0000.0870.3520.1210.0380.1050.0000.0000.0000.0000.0000.0530.0000.0750.0000.0000.0000.0000.0000.0000.0921.0000.0350.0000.0590.1030.0000.0470.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Br0.0550.1800.2020.0000.3390.0000.0000.0590.1470.0000.0000.0110.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.2600.1490.0000.0000.0000.0000.0380.0000.0000.0000.0000.1250.0960.0290.1820.0000.0000.0000.0000.0000.0450.1740.0000.0000.0000.0000.0000.0000.0880.0351.0000.0000.0560.0990.0000.0440.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
ClO40.0000.0000.1500.0000.1110.1380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
BF40.2550.0920.1040.0350.1340.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3670.0000.0000.0000.0550.0000.0000.0000.0000.0000.1380.0000.0000.0000.0000.0000.0320.1250.0120.2240.0130.0000.0000.0000.0000.1300.0000.0000.0000.0000.0000.0000.0000.0290.1200.0590.0560.0001.0000.1300.0130.0680.0000.0000.0000.0510.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
tf2n0.1080.0000.0000.1600.2060.0000.0000.0480.0000.0730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1550.0380.0320.0000.0420.0320.0000.0000.0000.0480.0880.0000.0330.0000.0390.0190.0000.0550.0670.1110.0490.0280.0000.0000.0000.0740.0790.0000.0000.0000.0160.0000.0410.0000.0000.1180.0000.0000.0700.1100.1030.0990.0000.1301.0000.0590.1130.0200.0000.0200.0930.0000.0000.0200.0320.0200.0530.0560.0420.0000.0080.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0370.000
SO3CF30.0000.0810.0000.0000.0000.0000.0000.0900.0000.0000.0490.0000.0000.0000.0000.0000.0000.1510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0610.1510.1040.1140.0000.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0600.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0130.0591.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
PF60.0000.0910.1690.0000.1230.0000.0000.0500.0000.0000.0940.0680.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0000.0690.0000.0000.0000.2000.0000.0000.0000.1240.0000.0860.2070.0310.0380.0000.0000.0000.0000.0800.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.1030.0470.0440.0000.0680.1130.0001.0000.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
CO2CF30.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1410.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0510.0500.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
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BF3CF30.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.3190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
BF3CF20.0680.1190.1170.5970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1480.0000.0000.0640.1870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0640.0810.2370.2380.0000.0000.0000.0000.0620.0000.3200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.6260.0290.0250.0000.0510.0930.0000.0380.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
I0.0000.0370.1830.0000.2160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.1120.0000.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.0000.0000.2370.0690.0760.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
BF3CH20.0000.1570.0000.0000.0000.0000.0000.1010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
N(SO2CF2)20.0000.0000.0000.4370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.8820.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
DCA0.0000.0000.0000.0000.2500.0000.0000.0000.0000.0760.0000.0000.0000.0000.0000.1190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0370.2660.2660.0000.0000.0190.0000.0000.0000.0000.0000.0000.0000.1810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
N(SO2F)20.0000.0580.0000.0000.1600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3940.3890.0000.0000.2240.0000.0000.0000.0000.0000.0000.1410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
CF2SO30.0000.0000.0000.7210.1150.0000.0000.0000.0000.0000.0000.0000.0000.2040.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4460.0000.0000.0000.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
CH2SO40.0000.1390.0000.4460.2530.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0130.0000.0850.0000.0000.0000.0000.0630.0000.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2290.0000.0000.0000.0030.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
MeSO30.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0760.0700.0350.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.2500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
SO3CH20.0000.2710.0000.0000.0000.0000.0000.1880.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.1890.1590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
MeSO40.0420.0810.0000.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0300.2420.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
FAP0.0000.0000.0000.0000.1280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0570.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
CTF30.0000.0810.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TCM0.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
TCB0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
AlCl40.0000.0000.0000.0000.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0880.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
SCN0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.1800.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
tosylate0.0000.0000.0000.0000.0930.0000.0000.0000.0000.0000.0760.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
N(SO2CH3)20.0000.0000.0000.0000.1510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1720.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1800.0870.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
H2PO40.1540.0140.0260.0000.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1160.1440.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
benzoate0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0330.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2730.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
N(SO2CF3)(COCF3)0.0700.0000.0000.0000.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.1390.0000.0000.0000.0000.0110.0680.0000.1720.1720.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
saccharate0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
TFPB0.0000.0000.1120.0000.0160.0000.0000.0520.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0560.0840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
HSO40.1050.0000.0000.0000.0650.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
acetylacetonate0.0000.3140.0000.1370.3630.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4610.0000.0000.0000.0000.0000.0000.3160.3030.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0680.0930.0390.0310.0000.0000.0000.0000.2250.0000.0000.0000.0000.0000.0000.3960.0000.0000.0000.2760.4610.0000.4610.0000.0000.3330.0000.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
AlBr40.1010.0630.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0000.0000.0070.1130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-01-31T17:15:53.798353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-31T17:15:56.825545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Im1Im12Im13Im123Py1Py13Py14Py135PyrPyr1Pyr11Pip11Pip113MorMor11Ox11Im12345BentrzTrz11NH3NH2NHAmmoniumdimethyl[(1R,2S,5R)-(-)menthoxymethyl]ammoniumGuancycGuanBThzPhosphoniumSulfoniumaCCH2aCCH3CYCCH3aNCH3aNCH2cycNHC3cycNCH2SCH3SCH2PCH3PCH2NCH3NCH2–CH3–CH2––OH–O––CN–CH2Cl–COOH–COO–CH–CCH–CH=CH2–PH–furan–thiophene–PH1235–tertHCOONO3–CF3–CF2–ClBrClO4BF4tf2nSO3CF3PF6CO2CF3acetateBF3CF3BF3CF2IBF3CH2N(SO2CF2)2DCAN(SO2F)2CF2SO3CH2SO4MeSO3SO3CH2MeSO4FAPCTF3TCMTCBAlCl4SCNtosylateN(SO2CH3)2H2PO4benzoateN(SO2CF3)(COCF3)saccharateTFPBHSO4acetylacetonateAlBr4exp (K)
11.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0345.15
21.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0314.15
31.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0430.15
41.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0310.15
51.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0282.15
61.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0284.15
71.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0357.15
81.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0389.15
91.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0324.15
101.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0250.15
Im1Im12Im13Im123Py1Py13Py14Py135PyrPyr1Pyr11Pip11Pip113MorMor11Ox11Im12345BentrzTrz11NH3NH2NHAmmoniumdimethyl[(1R,2S,5R)-(-)menthoxymethyl]ammoniumGuancycGuanBThzPhosphoniumSulfoniumaCCH2aCCH3CYCCH3aNCH3aNCH2cycNHC3cycNCH2SCH3SCH2PCH3PCH2NCH3NCH2–CH3–CH2––OH–O––CN–CH2Cl–COOH–COO–CH–CCH–CH=CH2–PH–furan–thiophene–PH1235–tertHCOONO3–CF3–CF2–ClBrClO4BF4tf2nSO3CF3PF6CO2CF3acetateBF3CF3BF3CF2IBF3CH2N(SO2CF2)2DCAN(SO2F)2CF2SO3CH2SO4MeSO3SO3CH2MeSO4FAPCTF3TCMTCBAlCl4SCNtosylateN(SO2CH3)2H2PO4benzoateN(SO2CF3)(COCF3)saccharateTFPBHSO4acetylacetonateAlBr4exp (K)
5910.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.01.06.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0278.15
5920.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.00.00.01.06.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0268.15
5930.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.01.010.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0360.15
5940.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.01.06.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0352.15
5950.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.00.01.08.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0349.15
5960.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0339.15
5970.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.04.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0428.15
5980.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02.04.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0303.15
5990.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0436.15
6000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.03.01.00.01.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0305.15